Book Image

Machine Learning Using TensorFlow Cookbook

By : Luca Massaron, Alexia Audevart, Konrad Banachewicz
Book Image

Machine Learning Using TensorFlow Cookbook

By: Luca Massaron, Alexia Audevart, Konrad Banachewicz

Overview of this book

The independent recipes in Machine Learning Using TensorFlow Cookbook will teach you how to perform complex data computations and gain valuable insights into your data. Dive into recipes on training models, model evaluation, sentiment analysis, regression analysis, artificial neural networks, and deep learning - each using Google’s machine learning library, TensorFlow. This cookbook covers the fundamentals of the TensorFlow library, including variables, matrices, and various data sources. You’ll discover real-world implementations of Keras and TensorFlow and learn how to use estimators to train linear models and boosted trees, both for classification and regression. Explore the practical applications of a variety of deep learning architectures, such as recurrent neural networks and Transformers, and see how they can be used to solve computer vision and natural language processing (NLP) problems. With the help of this book, you will be proficient in using TensorFlow, understand deep learning from the basics, and be able to implement machine learning algorithms in real-world scenarios.
Table of Contents (15 chapters)
5
Boosted Trees
11
Reinforcement Learning with TensorFlow and TF-Agents
13
Other Books You May Enjoy
14
Index

Declaring operations

Apart from matrix operations, there are hosts of other TensorFlow operations we must at least be aware of. This recipe will provide you with a quick and essential glance at what you really need to know.

Getting ready

Besides the standard arithmetic operations, TensorFlow provides us with more operations that we should be aware of. We should acknowledge them and learn how to use them before proceeding. Again, we just import TensorFlow:

import tensorflow as tf

Now we're ready to run the code to be found in the following section.

How to do it…

TensorFlow has the standard operations on tensors, that is, add(), subtract(), multiply(), and division() in its math module. Note that all of the operations in this section will evaluate the inputs elementwise, unless specified otherwise:

  1. TensorFlow provides some variations of division() and the relevant functions.
  2. It is worth mentioning that division() returns the same...